Web Mining and Social Networking: Techniques and ... - tud.ttu.ee

52 3 Algorithms and Techniquesuses these to modify its weights, and it aims to match its classifications with the targets in thetraining set. Weight modification follows a learning rule. The pseudo code of training neuralnetwork is shown in Algorithm 3.6.Algorithm 3.6: Training neural network algorithmfully trained = FALSE;While fully trained != TRUE dofully trained = TRUE;For each training vector = < X 1 ,X 2 ,...,X n ,θ,target > doa = (X 1 ∗W 1 )+(X 2 ∗W 2 )+...+(X n ∗W n ) − θ;y = sigmoid(a);If y != target then fully trained = FALSE;For each W i doMODIFY WEIGHT( W i );endendIf fully trained then BREAK;endThe advantage of Neural nets is that it performs very well on difficult, non-linear domains,where it becomes more and more difficult to use Decision tree, or Rule induction systems,which cut the space of examples parallel to attribute axes. One of disadvantages in usingNeural nets for data mining is a slow learning process, compared top for example Decisiontrees. This difference can very easily be several orders of magnitude. Another disadvantageis that neural networks do not give explicit knowledge representation in the form of rules, orsome other easily interpretable form. The model is implicit, hidden in the network structureand optimized weights, between the nodes.In addition to the above commonly used supervised learning methods, there are manyothers developed, such as Discriminative Classi f ication, Maximum Entropy Learners, SV M,and so forth. Refer [33] for a more comprehensive survey.3.3 Unsupervised LearningIn this section, we will introduce major techniques of unsupervised learning (or clustering).Among a large amount of approaches that have been proposed, there are three representativeunsupervised learning strategies, i.e., k-means, hierarchical clustering and density basedclustering.3.3.1 The k-Means AlgorithmK-means clustering algorithm was first proposed in [173, 110]. In briefly, k-means clusteringis a top-down algorithm that classifies the objects into k number of groups with regard toattributes or features, where k is a positive integer number and specified apriori by users.The grouping is done by minimizing the sum of squares of distances between object and thecorresponding cluster centroid.